High-Speed and Accurate Meat Composition Imaging by Mechanically-Flexible Electrical Impedance Tomography With k-Nearest Neighbor and Fuzzy k-Means Machine Learning Approaches

P N Darma,M Takei

doi:10.1109/access.2021.3064315

Abstract

High-speed and accurate meat composition imaging method has been proposed based on mechanically-flexible electrical impedance tomography (mech-f-EIT) with k-nearest neighbor and fuzzy k-means machine learning approaches. This proposed method has four stages which are 1) estimation of meat boundary shape ∂Ω by mech-f-EIT for base data, 2) approximation of Jacobian matrix J* by k-nearest neighbor (k-NN) algorithm under ∂Ω for high speed, 3) clustering of meat composition k σ (fat k = 1, lean k = 2, bone k = 3) by fuzzy k-means algorithm based on the reconstructed meat conductivity distribution σ for high accuracy, and 4) edge detection of meat composition k Ω by Canny algorithm for sharp edge. This method is qualitatively evaluated by using two agar phantoms, a cow's lower leg and three lamb's lower legs. As the results, mech-f-EIT estimates ∂Ω with total mean boundary error 〈ẽ b 〉 = 4.81 %. This method achieves high-speed approximation of J* with total mean speed-up performance 〈s̃p〉 = 4.51 times as compared with the computation time of standard J; nonetheless, total mean cross correlation between J* and J is accurate 〈c̃ c 〉 = 0.92. Moreover, this method clusters the k σ with total mean area error 〈ẽ a 〉 = 4.49 %. Furthermore, this imaging method detects sharply the meat composition edges k Ω between fat and lean (k = 1 - 2) and between lean and bone (k = 2 - 3) with total mean edge error 〈ẽ e 〉 =6.90 %.

Highlights

In meat industries, automatic meat cutting machine are already put into practice for alleviating the working environment [1]
(mech-f-EIT) which is composed of A) outer frame to attach electrical cables, B) inner frame with radius R to fix sensor position, C) Q number electrodes eq for current injection and voltage measurement (Q = 16 as an example in the figure), D) Q number axes length meter αq to measure the meat axes lengths rq and E) platform to hold the meat firmly in position
The imaging area inside the mech-f-EIT is divided into Q quadrants qQ(Q =16 in the figure as an example from q1 to q16)

Summary

Introduction

Automatic meat cutting machine are already put into practice for alleviating the working environment [1]. The meat cutting machine require the highspeed and accurate clustering and edge detection of meat composition of fat, lean and bone to improve the cutting efficiency and quality. The cutting machine uses X-ray for the clustering and edge detection. Dual energy X-ray absorptiometry (DEXA) is used for clustering and edge detection of the meat composition [2], and the cross-sectional composition imaging [3]. Magnetic resonance imaging (MRI) visualizes cross sectional images of living pig loin to predict body composition [4].

Methods

Results

Conclusion